DADA 2020: The 2nd IEEE International Workshop on Deep Analysis of Data-Driven Applications

Call for Papers

Goal of the workshop:

The workshop aims to identify challenging problems and novel applications of deep learning algorithms to
address the problems. The workshop fosters deep learning techniques to modeling and analyzing data-driven
applications. It creates a venue for researchers from academia and industry to share their intellectual ideas and experiences with respect to challenges, novel problems, innovative applications, and creative solutions
regarding the applications of deep learning to scientific and industrial problems driven by data.

Workshop theme:

AI-based application systems are becoming the mainstream for software industry. The recent advancement in big data generation and management has created an avenue for decision makers to utilize these huge data
collected from many application domains for different purposes. Application developers and data scientists have utilized conventional machine learning techniques for a long time. However, with the advancement of deep learning paradigm, developers and decision makers are able to learn more about their data and then explore and model hidden features for prediction and analysis purposes. The new trends of practices in developing data driven application systems and decision making algorithms seek adaptation of deep learning algorithms and techniques in many application domains including AI-based software systems and applications and variety of scientific domains. An interesting aspect of adaptation of deep learning algorithms to such problems is that new challenging problems can be identified and deep learning algorithm can be innovatively adapted to address the discovered problems are explored.

Scope of the workshop:

Researchers and practitioners all over the world, from both academia, research institute, and industry, working in the area of data analysis and data-driven application domains using deep learning approaches are invited to discuss the state of the art solutions, novel issues, recent developments, applications, methodologies, techniques, experience reports, and tools for the development and use of deep learning in their application domains. Topics of interest include, but are not limited to, the following applications of deep learning to:

• Intelligent data analysis
• AI-based software development
• Smart businesses and intelligent financial systems and applications
• Time series modeling and prediction
• Generative adversarial modeling of problems
• Natural language processing
• Security and privacy
• Attention-based networks
• Transfer learning

Likely participants: Machine and deep-learning experts, data scientists, software engineers, researchers and developers are called to participate and exchange ideas and techniques.

EasyChair – Paper Submission Site

Submission deadline date extended to May 1, 2020

Workshop Chair

Siami Namin, Texas Tech University

Program Committee

Mikio Aoyama, Nanzan University, Japan
Sara Sartoli, University of North Georgia, USA
Chihiro Shibata,  Tokyo University of Technology, Japan
Sima Siami-Namini, Texas Tech University, USA
Neda Tavakoli, Georgia Institute of Technology, USA
Aerambamoorthy Thavaneswaran, University of Manitoba, Canada
Ali Saman Tosun, University of Texas at San Antonio, USA